The June 1995 Scientific American includes an article From
Complexity to Perplexity by John Horgan. Subtitled ``Can science
achieve a unified theory of complex systems? Even at the Sante Fe
Institute, some researchers have their doubt'' the article is very
critical of artificial life and related disciples. I highly recommend
it. Below I excerpt some points, and comment on them, [concluding
what?]

The grandest claim of Santa Fe'ers is that they may be able to
construct a ``unfied theory'' of complex systems. John H Holland, a
computer scientist with join appointments at the University of
Michigan and the Santa Fe Institute, spelled out this breathtakingly
ambitions vision in a lecture two years ago: ``Many of our most
troubling long-range problems--trade balances, unsustainability, AIDS,
genetic defects, mental health, computer viruses--center on certain
systems of extraordinary complexity. The systems that host these
problems--economies, ecologies, immune systems, embryos, neverous
systems, computer networks--appear to be as diverse as the problems.
Despite appearances, however, the systems do share significant
characteristics, so much so that we group them under a single
classification at the Santa Fe Insitute, calling them complex adaptive
systems [CAS]. This is more than terminology It signals our
intuition that there are general principles that govern all CAS
behavior, principles that point to ways of solving the attendant
problems.'' Holland, it should be said, is considered to be one of
the more modest complexologists.

Some workers now disavow the goal of a unified theory. ``I don't even
know what that would mean,'' says Melanie Mitchell, a former student
of Holland's who is now at the SFI. ``At some level you can say all
complex systems are aspects of the same underlying principles, but I
don't think that will be very useful.'' Stripped of this vision of
unification, however, the Santa Fe Institute loses much of its luster.
It becomes just another place where researchers are using computers
and other tools to adress problems in their respective fields.
Aren't all scientists doing that?

Building computer models of natural systems is in vogue all over
science. An `applied computer science' where the principles of the
implementation and analysis of models of complex systems are studied
is useful. To what extent can one perform an experiment with the
model instead of the real thing?

How is the field of complexity any different from numerical analysis?

What kind of general principles and unified theories are the immodest
talking about? I don't know. If they think they can formalize
(compile away) layers of complexity, they are indeed wrong. Murray
Gell-Mann, a founder of the SFI makes some claims in his book The Quark and the Jaguar.

[Herbert Simon says ``Most of the people who talk about these
great theories have been infected with mathematics,'']

This is one of several swipes at math. I can see the point: the
mathematician solves equations and proves theorems. The problem is
disposed of and we move onward. It's silly to suggest this will
happen to trade balances, mental health, viruses, etc.

... the awkward fact that complexity exists, in some murky
sense, in the eye of the beholder.

Yes, absolutely. Compexity is relative to an observer (see here). That doesn't mean that it is an unfit target of
science---everything is at least a little relative (except maybe
math).

Artificial life--and the entire field of complexity--seems to
be based on a seductive syllogism: There are simiple sets of
mathematical rules that when followed by a computer give rise to
extrememly complicated patterns. The world also contains many
extrememly complicated patterns. Conclusion: Simple rules underlie
many extrememly complicated phenomena in the world. With the help of
powerful computers, scientists can root those rules out.

All phenomena in the world are the result of simple rules: the
field equations of quantum physics. Sometimes complexity appears in a
`single level' from many applications of simple rules over a simple
system, the kind of thing we can model. Such layers are the subject of
complexologists. Surely many `natural systems' of the world are `big
balls of complexity' with no layers and no hope of an efficient model.
But the general success of scientific models demonstrates that
important systems lie within the scope.

This syllogism was refuted in a brilliant paper published in
Science last year. The authors, led by philosopher Naomi Oreskes
of Darmouth College, warn that ``verification or validation of
numerical models of natural systems is impossible.'' The only
propositions that can be verified--that is, proved true--are those
concerning ``closed'' systems, based on pure mathematics and logic.
Natural systems are open: our knowledge of them is always partial,
approximate, at best.

So what? Who claims otherwise? This repeats the above error of
throwing out complexity because it has been `polluted' with
relativism. As if the rest of science stood on firm ground...

``Like a novel, a model may be convincing --it may ring true if
it is consistent with our experience of the natural world,'' Oreskes
and her colleagues state. ``But just as we may wonder how much the
characters in a novel are drawn from real life and how much is
artifice, we might ask the same of a model: How much is based on
observation and measurement of accessible phenomena, how much is based
on informed judgement, and how much is convenience?''

Do you trust the Turing test? If it quacks...

[distribution of critical states over time follows a Power
law]

I think there's something to this; that is one of the meanings of the
word `pink' in bomb's iconic language.

... the [sandpile model with Power behavior] may be so general
and so statistical in nature that it cannot really illuminate even
those systems it describes. After all, many phenomena can be
described by a Gaussian or bell curve.

Yes. And just like there's a theorem behind the bell curve (the
Strong Law of Probability), perhaps there's a theorem behind Power law
behavior.

Another skeptic is Philip W. Anderson ... particle physics and
indeed all reductionist approaches have only a limited ability to
explain the world. Reality has a hierarchical structure, Anderson
argued, with each level independent, to some degree, of the levels
above and below. ``At each stage, entirely new laws, concepts and
generalizations are necessary, requiring inspiration and creativity to
just as great a degree as in the previous one,'' Anderson noted.
``Psychology is not applied biology, nor is biology applied
chemistry.''

New laws are only needed because of our limited mental capacity.
complex systems are exactly those things that defy ability to
relate both ends. If there's really anything new at any level, then
this is evidence of a god.

One reason that there are more simple systems than you might expect or
guess is that a simple system can appear on top of a complex system,
if we use a shoe-horn, idealizing the complex. Thus weather
simulations can work by idealizing the atmosphere, or a population
dynamics system by idealizing reproducing individuals, a neural net
with Hebbs neurons, or genes, or... Is it really `simple'? Is it a
scientific model? it can't work just right because we've thrown out
some information, but it might work well enough to be useful.

Anderson favors the view of nature described by the
evolutionary biologist Stephen Jay Gould of Harvard, who emphasizes
that life is shaped less by deterministic laws than by contingent,
unpredictable circumstances. [i'm in favor of natural history]

Stuart Kauffman thinks his simulations may lead to the
discovery of a ``new fundamental force'' that counteracts the
universal drift towards disorder required by the second law of
thermodynamics.

In a book to be published later this year, At Home in the
Universe, Kauffman asserts that both the origin of life on the earth
and its subsequent evolution were not ``vastly improbably'' but in
some fundamental sense inevitable; life, perhaps similar to ours,
almost certainly exists elsewhere in the universe.

Yes! I do not believe the life force exists like gravity or magnetism.
It's more like statistics, perhaps the Strong Law. Of course
Kauffman's `similar to ours' is meaningless. I'm not too familiar
with the term, but `extropy' sounds about right.

... the hope that as computers grow in power, so will science's
ability to predict, control and understand nature. Others demurred.
Roger N Shepard, a psychologist at Stanford University, worried that
even if we can capture nature's intricacies on computers, whose models
might themselves be so intricate that they elude human understanding.
Francisco Antonio Doria, a Brazilian mathematician, smiled ruefully
and murmured, ``We go from complexity to perplexity.'' Everyone
nodded.

there's no doubt that more powerful computers will allow better
modeling and the benefits thereof. Even though we may never
`understand' one of our models---because it's complex beyond our
minds, its still useful! Our perplexity is really wonderment that
`that's all there is to it'.